R Notebook

install.packages("tidyverse") ## instalacja pakietu

trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-x86_64/contrib/4.3/tidyverse_2.0.0.tgz'
Content type 'application/x-gzip' length 428408 bytes (418 KB)
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downloaded 418 KB

The downloaded binary packages are in
    /var/folders/zs/2z4rlxv54pv7ml4jld2kccmc0000gn/T//RtmpWuGTkU/downloaded_packages

library(tidyverse)

── Attaching core tidyverse packages ─────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.3     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     ── Conflicts ───────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(readxl)

rent <- read_excel(path = "../data-raw/rent-poznan.xlsx")
rent <- as.data.frame(rent)
head(rent)

Wybieramy kolumny z funkcją select z pakietu dplyr.

rent |> select(id, price, flat_area)

rent |> select(1, 5, 10)

rent |> select(1, 5, price)

rent |> select(cena=price, pow=flat_area)

rent |> select(starts_with("p"), ends_with("id"), flat_for_students)

rent |> select(price:flat_for_students, -flat_rooms, -flat_build_year)

rent |> select(contains("ri")) 

rent |> select_if(is.character)

1. wybrać kolumny, które mają w nazwie “ad”

rent |> select(contains("ad"))

2. wybrać kolumny od flat_heating do flat_balcony bez kolumny ad_promo

rent |> select(flat_heating:flat_balcony, -ad_promo)

Wybieramy wiersze z funkcją filter

rent |> filter(price < 500)

rent |> filter(price < 500 & flat_rooms == 1)

rent |> filter(price < 500, flat_rooms == 1)

rent |> filter(price > 5000 | price < 200)

rent |> filter(between(price, 1000, 1200)) ## to i to poniżej jest tym samym

rent |> filter(price >= 1000, price <= 1200)

rent |> filter(price < mean(price))

rent |> filter(price < mean(price), .by = individual)

Zadania: wybranie wierszy po dzielcy: Rataje lub Winogrady

rent |> filter(quarter %in% c("Rataje", "Winogrady"))

Wstawianie nowych kolumn z funkcją mutate (transmute)

rent |> mutate(pricem2 = price/flat_area,
               pricem2_log = log(pricem2))

rent |> mutate(pricem2_gr = mean(price), .by = quarter)

rent |> mutate_at(vars(price, flat_area), log)

rent |> mutate_if(is.logical, as.numeric)